@inproceedings{f551d0dd76784fdcb626248fed1bfc92,
title = "Efficient robust digital hyperplane fitting with bounded error",
abstract = "We consider the following fitting problem: given an arbitrary set of N points in a bounded grid in dimension d, find a digital hyperplane that contains the largest possible number of points. We first observe that the problem is 3SUM-hard in the plane, so that it probably cannot be solved exactly with computational complexity better than O(N2), and it is conjectured that optimal computational complexity in dimension d is in fact O(N d). We therefore propose two approximation methods featuring linear time complexity. As the latter one is easily implemented, we present experimental results that show the runtime in practice.",
keywords = "approximation, digital hyperplane, fitting, linear programming, randomization",
author = "Dror Aiger and Yukiko Kenmochi and Hugues Talbot and Lilian Buzer",
year = "2011",
month = apr,
day = "18",
doi = "10.1007/978-3-642-19867-0_19",
language = "English",
isbn = "9783642198663",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "223--234",
booktitle = "Discrete Geometry for Computer Imagery - 16th IAPR International Conference, DGCI 2011, Proceedings",
note = "16th IAPR International Conference on Discrete Geometry for Computer Imagery, DGCI 2011 ; Conference date: 06-04-2011 Through 08-04-2011",
}